In-Office RL Engineer for Humanoid Autonomy

Figure; Sacramento (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Figure is an AI robotics company building autonomous humanoid robots. The Helix AI Engineer will develop reinforcement learning systems that enable robots to learn from interaction across simulation and real-world environments, with a focus on long-horizon decision making and robustness.

Collaboration across teams is essential to integrate RL into the autonomy stack and scale learning systems. Ideal candidates have strong RL fundamentals, experience in Python and PyTorch, and a track record of

Qualifications

• Experience developing and applying reinforcement learning algorithms in complex environments.
• Strong understanding of RL fundamentals (policy optimization, value methods, model-based RL).
• Experience training policies in simulation and/or real-world systems.
• Proficiency in Python and deep learning frameworks such as PyTorch.
• Experience with large-scale experimentation and distributed training systems.
• Strong experimental rigor and ability to diagnose and improve learning systems.
• Solid software engineering skills and ability to build scalable, reliable systems.
• Ability to operate independently and drive ambiguous, high-impact technical problems.

Responsibilities

• Design and implement reinforcement learning algorithms for embodied agents operating in real-world and simulated environments
• Train policies that learn from interaction, feedback, and large-scale experience across diverse tasks
• Develop reward modeling, credit assignment, and exploration strategies for complex, long-horizon behaviors
• Improve policy robustness to real-world challenges such as noise, partial observability, and environment variability
• Work across online and offline RL settings, including learning from large-scale logged robot data
• Collaborate closely with pretraining, video, generative, agent, and robot learning teams to integrate RL into the full autonomy stack
• Build scalable training systems for RL, including distributed rollouts, simulation infrastructure, and experiment management
• Design evaluation frameworks to measure policy performance, stability, and generalization

Skills

Reinforcement learning

Python

PyTorch

Job description

Figure is an AI robotics company building autonomous humanoid robots. The Helix AI Engineer will develop reinforcement learning systems that enable robots to learn from interaction across simulation and real-world environments, with a focus on long-horizon decision making and robustness.

Collaboration across teams is essential to integrate RL into the autonomy stack and scale learning systems. Ideal candidates have strong RL fundamentals, experience in Python and PyTorch, and a track record of